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About:
Analyzing hCov genome sequences: Applying Machine Intelligence and beyond
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covidontheweb.inria.fr
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research paper
schema:ScholarlyArticle
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Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
Analyzing hCov genome sequences: Applying Machine Intelligence and beyond
Creator
Rahman,
Rahman, M
Anjum, Naser
Haisam, Muhammad
Rafid, Mohammad
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source
BioRxiv
abstract
Covid-19 pandemic, caused by the sars-cov-2 strain of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analysed. We present here an analysis pipeline comprising phylogenetic analysis on strains of this novel virus to track its evolutionary history among the countries uncovering several interesting relationships, followed by a classification exercise to identify the virulence of the strains and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.
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2020-06-03
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bibo:doi
10.1101/2020.06.03.131987
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biorxiv
sha1sum (hex)
ed6efa757ae5582f4a6108e97d2f40c7cb320584
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https://doi.org/10.1101/2020.06.03.131987
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Analyzing hCov genome sequences: Applying Machine Intelligence and beyond
schema:publication
bioRxiv
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covid:ed6efa757ae5582f4a6108e97d2f40c7cb320584#body_text
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named entity 'power'
named entity 'strain'
named entity 'PHYLOGENETIC ANALYSIS'
named entity 'INTERESTING'
named entity 'WHAT'
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